Potential of an activity index combining acceleration and location for automated estrus detection in dairy cows

The objective of this study was to assess the effectiveness and practicability of an activityindex combining acceleration and location data for automated estrus detection in dairycows. By using a wearable neck tag, measurements of acceleration and location were gathered from 22 multiparous cows monitored incessantly for 6 days to derive activity recordsof each cow. The maximum-minimum distance clustering (MMDC) method was used todivide hourly activity data into low, medium, high, and intensity level groups. The weightedsum of the proportions of the low, medium, high, and intensity activities in an hour constituted the activity level. The activity index was defined as the ratio of the variation inhourly activity level compared to the same time period during the previous three days. Furthermore, whether the cow was in estrus was judged above a set threshold. The studyshowed that the power consumption and communication effects of the neck tags wereacceptable for indoor-housing conditions. For the two consecutive time periods, theactivity-index-based detection algorithm achieved 90.91% for accuracy, 100% for precision,100% for specificity, 83.33% for recall, 90.91% for F1 score, and 0.82 for Kappa coefficient. Onthe basis of these results, it can be concluded that the combination of acceleration andlocation in the activity index can promote estrus detection in dairy cows.